Three ways to use rmarkdown

  1. Reporting conclusions without going into the code
  2. collaboration with others who might be interested in both the conclusions you draw and code behind.
  3. lab notebook to record what you did and the thinking process behind

Basic parts of an rmarkdown document

1. Text with simple formatting

enclose with example
* or _ italics
** or __ bold
|nrow(mtcars)`
^ 102
~ log10

[Rmd cheatsheet][https://www.rstudio.com/wp-content/uploads/2016/03/rmarkdown-cheatsheet-2.0.pdf]

2. code chunk

Add with one of the 3 ways:

  • Cmd/Ctrl + Alt + I
  • Insert button
  • typing in chunk delimiter
print("I am the output of this code chunk.")
## [1] "I am the output of this code chunk."

chunk options

ggplot(mtcars, aes(x = cyl, y = mpg)) + 
  geom_point()

Caching

Be careful about caching + good for loading up computationally expensive output - not based on dependencies

data = mtcars[1:10, ]
proc = dplyr::filter(data, am == 1)
head(proc)
##                mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4     21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710    22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
data = mtcars[11:20, ]
proc = dplyr::filter(data, am == 1)
head(proc)
##                 mpg cyl disp hp drat    wt  qsec vs am gear carb
## Fiat 128       32.4   4 78.7 66 4.08 2.200 19.47  1  1    4    1
## Honda Civic    30.4   4 75.7 52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla 33.9   4 71.1 65 4.22 1.835 19.90  1  1    4    1

Global options

defines default for every chunk

knitr::opts_chunk$set(echo = FALSE)

Inline code

There are a total of 10 cars in the dataset with an average weight of 3.7 lbs

4. YAML header

Params

useful for re-rendering same report for different parameters

## # A tibble: 6 x 11
##   manufacturer model displ  year   cyl trans      drv     cty   hwy fl    class 
##   <chr>        <chr> <dbl> <int> <int> <chr>      <chr> <int> <int> <chr> <chr> 
## 1 audi         a4      1.8  1999     4 auto(l5)   f        18    29 p     compa…
## 2 audi         a4      1.8  1999     4 manual(m5) f        21    29 p     compa…
## 3 audi         a4      2    2008     4 manual(m6) f        20    31 p     compa…
## 4 audi         a4      2    2008     4 auto(av)   f        21    30 p     compa…
## 5 audi         a4      2.8  1999     6 auto(l5)   f        16    26 p     compa…
## 6 audi         a4      2.8  1999     6 manual(m5) f        18    26 p     compa…

For the dataset above, you will find that it has 7 unique classes. To generate the same report for values from each class, params can be declared in the header.

## Output formats Rmarkdown can the generate the following document types: - pdf_document (requires LaTex) - word_document - odt_document - rtf_document - md_document - github_document

How to generate output format?

  • change output in YAML
  • rmarkdown::render()
  • knit dropdown

output options

check parameters associated with specific outputs and change it by expanding output field

More output formats

website (rmarkdown::render_site())

Graphics for Communication

Labels

Add descriptive plot title

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Add subtitles and/or captions

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Replace axis or legend titles with labs()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

add math formulas with quote()

Annotations

label individual observations or groups with geom_text()

highlight text by adding boxes with geom_label()

automatically adjust labels to prevent overlap using ggrepel pacakge and add layer to highlight points

replace legend with labels directly on plot

add text in plot border with Inf and wrap text using or stringr::str_wrap()

use vjust and hjust to adjust position of your labels or text combinations of hjust and vjust

Scales

adjust your axis ticks for better visualisation, suppress labels with NULL

adjust breaks especially when you have sparse data points

use theme to adjust legend position

use guides() to control legend display

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Replace entire scale

adjusting color with RColorBrewer

use color-blind friendly theme

use scale_color_manual to use predefined mapping between values and colors

adjust continuous color scales with scale_color_gradient() or scale_fill_gradient() or viridis package

Zooming

zoom into a plot region using coord_cartesian

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Adjust plot limits to allow comparison on the same scale

Expand scale of the 2nd plot so that it is comparable

Themes

by default, there are 8 themes in ggplot2, use ggthemes to add extra themes

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Saving your plots

use ggsave or knitr 

## Saving 7 x 5 in image
adjust figure sizing with fig.width or out.width for output and add captio with fig.cap
cars plot

cars plot